Systems and methods for displaying object box in a video

ABSTRACT

A system and method for rendering an object box of an object in an image is provided in the present disclosure. The method includes obtaining a plurality of images, in a temporal sequence, each of the plurality of images relating to an object. The method also includes obtaining a first processed image by performing a smart algorithm analysis on a first image in the plurality of images, the smart algorithm analysis including identifying the object in the first image and determining a first coordinate of the object in the first image. The method further includes determining a first pixel distance between the object in two adjacent images in the plurality of images and rendering an object box for the object in each of the plurality of images for display based on the first coordinate of the object in the first image and the first pixel distance.

CROSS REFERENCE

This application is a continuation application of InternationalApplication No. PCT/CN2019/089977, filed on Jun. 4, 2019, which claimspriority of Chinese Patent Application No. 201810575791.9 filed on Jun.6, 2018, the entire contents of each of which are hereby incorporated byreference.

TECHNICAL FIELD

This disclosure generally relates to methods and systems for imageprocessing, and more particularly, to methods and systems for smoothlydisplaying object boxes in one or more images in a video.

BACKGROUND

Nowadays, crowded places such as subway or railway stations are oftenequipped with security screening equipment, usually positioned at theentrances to carry out safety inspections on various objects carried bypeople. While conducting safety inspections, the security screeningequipment captures a plurality of images in a temporal sequence, usuallyin the form of a video, and display the plurality of images in thetemporal sequence including various objects, sometimes with object boxesfor specific objects (e.g., something dangerous) on a display screen ofthe security screening equipment, so that the security staff can timelydetermine whether there is a prohibited object. In order to smoothlydisplay the object boxes, each image of the plurality of images capturedby the security screening equipment should be processed and analyzed toidentify the specific objects. However, this would increase the burdenof the processors and decrease the processing efficiency. If only a partof the plurality of images are processed and the rendering the objectboxes in the part of the plurality of images, the display is often notsmooth, showing glitches or mismatching the object box with the object,and making it more difficult for identification of dangerous objects.Thus, there is a need to smoothly display the objects with object boxesin the images captured during security screening with increasedprocessing efficiency and decreased computing burden of the processor.

SUMMARY

Embodiments of the present disclosure provide a method, a system, and acomputer readable storage medium for rendering an object box of anobject in an image. Specifically, it may include the following aspects.

In a first aspect, the present disclosure discloses a system. The systemmay include at least one storage medium including a set of instructionsand at least one processor in communication with the at least onestorage medium, wherein when executing the set of instructions, the atleast one processor may effectuate operations including obtaining aplurality of images, in a temporal sequence, each of the plurality ofimages relating to an object. The operations may also include obtaininga first processed image by performing a smart algorithm analysis on afirst image in the plurality of images, the smart algorithm analysisincluding identifying the object in the first image and determining afirst coordinate of the object in the first image. The operations mayfurther include determining a first pixel distance between the object intwo adjacent images in the plurality of images, and rendering an objectbox for the object in each of the plurality of images for display basedon the first coordinate of the object in the first image and the firstpixel distance.

In some embodiments, the system may also include an image capturedevice, wherein the plurality of images are obtained from a videocaptured by the image capture device.

In some embodiments, the operations may also include determining animaging range of the image capture device and a size of the image, anddetermining a pixel size of the image based on the imaging range and thesize of the image. The operations may include determining a timeinterval of the two adjacent images based on a first frame rate of thevideo, and determining a moving distance of the object located on aconveyor belt based on the time interval and a speed of the conveyorbelt. The operations may further include determining the first pixeldistance based on the moving distance and the pixel size.

In some embodiments, the operations may also include acquiring a secondimage from the plurality of images, wherein there is one or more imagesbetween the first image and the second image, and determining a secondprocessed image by perform the smart algorithm analysis on the secondimage. The operations may include determining a second pixel distancebetween the object in the first image and the object in the second imagebased on the first coordinate of the object in the first image and asecond coordinate of the object in the second image, and determining anumber count of the one or more images between the first image and thesecond image. The operations may further include determining the firstpixel distance based on the second pixel distance and the number countof the one or more images between the first image and the second image.

In some embodiments, the number count of the one or more images betweenthe first image and the second image is determined based on a firstframe rate of the video and a second frame rate of processed image,wherein the first frame rate and the second frame rate may be preset bya user.

In some embodiments, the operations may also include determining a thirdcoordinate of the object in each of the one or more images between thefirst image and the second image based on the first coordinate of theobject in the first image and the first pixel distance, and rendering anobject box in the first image based on the first coordinate of theobject in the first image. The operations may further include renderingan object box in each of the one or more images between the first imageand the second image based on the third coordinate, and rendering anobject box in the second image based on the second coordinate of theobject in the second image.

In some embodiments, the operations may also include determining afourth coordinate of the object in each of the plurality of images otherthan the first image based on the first coordinate of the object in thefirst image and the first pixel distance, and rendering an object box inthe first image based on the first coordinate of the object in the firstimage. The operations may further include rendering an object box ineach of the plurality of images other than the first image based on thefourth coordinate of the object.

In some embodiments, a shape of the object box may include one ofrectangle, square, triangle, circle, oval, or irregular shape.

In some embodiments, a shape of the object box may include a contour ofthe object.

In some embodiments, the smart algorithm analysis may include aconvolutional neutral network (CNN), Region-based Convolutional Network(R-CNN), Spatial Pyramid Pooling Network (SPP-Net), Fast Region-basedConvolutional Network (Fast R-CNN), Faster Region-based ConvolutionalNetwork (Faster R-CNN).

In some embodiments, the system may also include a screen configured todisplay the plurality of images and the object box in each of theplurality of images in the temporal sequence.

In some embodiments, the screen may display the plurality of images andthe object box according to a third frame rate.

In a second aspect, the present disclosure discloses a process. Theprocess may include obtaining a plurality of images, in a temporalsequence, each of the plurality of images relating to an object. Theprocess may also include obtaining a first processed image by performinga smart algorithm analysis on a first image in the plurality of images,the smart algorithm analysis including identifying the object in thefirst image and determining a first coordinate of the object in thefirst image. The process may further include determining a first pixeldistance between the object in two adjacent images in the plurality ofimages, and rendering an object box for the object in each of theplurality of images for display based on the first coordinate of theobject in the first image and the first pixel distance.

In a third aspect, the present disclosure discloses a non-transitorycomputer readable medium storing instructions, the instructions, whenexecuted by a computing device including at least one processor, causingthe computing device to implement a process. The process may includeobtaining a plurality of images, in a temporal sequence, each of theplurality of images relating to an object. The process may also includeobtaining a first processed image by performing a smart algorithmanalysis on a first image in the plurality of images, the smartalgorithm analysis including identifying the object in the first imageand determining a first coordinate of the object in the first image. Theprocess may further include determining a first pixel distance betweenthe object in two adjacent images in the plurality of images, andrendering an object box for the object in each of the plurality ofimages for display based on the first coordinate of the object in thefirst image and the first pixel distance.

In a fourth aspect, the present disclosure discloses a system includingat least one processor and a storage device. The system may include anacquisition unit configured to obtain a plurality of images, in atemporal sequence, each of the plurality of images relating to anobject. The system may also include an analysis unit configured toobtain a first processed image by performing a smart algorithm analysison a first image in the plurality of images, the smart algorithmanalysis including identifying the object in the first image anddetermining a first coordinate of the object in the first image. Thesystem may also include a distance determination unit configured todetermine a first pixel distance between the object in two adjacentimages in the plurality of images. The system may further include arendering unit configured to render an object box for the object in eachof the plurality of images for display based on the first coordinate ofthe object in the first image and the first pixel distance.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. The drawings are not to scale. Theseembodiments are non-limiting exemplary embodiments, in which likereference numerals represent similar structures throughout the severalviews of the drawings, and wherein:

FIG. 1 is a schematic diagram illustrating an exemplary securityscreening system according to some embodiments of the presentdisclosure;

FIG. 2 is schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary computing device according to someembodiments of the present disclosure;

FIG. 3 is a schematic diagram illustrating exemplary components of anexemplary user device according to some embodiments of the presentdisclosure;

FIG. 4 is a block diagram of an exemplary image processing deviceaccording to some embodiments of the present disclosure;

FIG. 5 is a block diagram of an exemplary processing module according tosome embodiments of the present disclosure;

FIG. 6 is a flow chart illustrating an exemplary process for renderingobject boxes according to some embodiments of the present disclosure;

FIG. 7 is a flow chart illustrating an exemplary process for determininga first pixel distance between an object in two adjacent imagesaccording to some embodiments of the present disclosure;

FIG. 8 is a flow chart illustrating another exemplary process fordetermining a first pixel distance between an object in two adjacentimages according to some embodiments of the present disclosure;

FIG. 9 is a flow chart illustrating another exemplary process forrendering object boxes according to some embodiments of the presentdisclosure;

FIG. 10 is a block diagram of an exemplary rendering unit according tosome embodiments of the present disclosure;

FIG. 11A illustrates a schematic image captured by the securityscreening system according to some embodiments of the presentdisclosure;

FIG. 11B illustrates a schematic processed image determined by thesecurity screening system based on the image shown in FIG. 11A accordingto some embodiments of the present disclosure;

FIG. 12 illustrates a schematic image shown in FIG. 11A and the objectbox displayed on a screen according to some embodiments of the presentdisclosure; and

FIG. 13 is a schematic diagram illustrating determination of the firstpixel distance between an object in two adjacent images according tosome embodiments of the present disclosure.

DETAILED DESCRIPTION

In order to illustrate the technical solutions related to theembodiments of the present disclosure, brief introduction of thedrawings referred to in the description of the embodiments is providedbelow. Obviously, drawings described below are only some examples orembodiments of the present disclosure. Those having ordinary skills inthe art, without further creative efforts, may apply the presentdisclosure to other similar scenarios according to these drawings.Unless stated otherwise or obvious from the context, the same referencenumeral in the drawings refers to the same structure and operation.

As used in the disclosure and the appended claims, the singular forms,“an,” and “the” include plural referents unless the content clearlydictates otherwise. It will be further understood that the terms“comprises,” “comprising,” “includes,” and/or “including” when used inthe disclosure, specify the presence of stated steps and elements, butdo not preclude the presence or addition of one or more other steps andelements.

Some modules of the system may be referred to in various ways accordingto some embodiments of the present disclosure, however, any number ofdifferent modules may be used and operated in a client terminal and/or aserver. These modules are intended to be illustrative, not intended tolimit the scope of the present disclosure, Different modules may be usedin different aspects of the system and method.

According to some embodiments of the present disclosure, flow charts areused to illustrate the operations performed by the system. It is to beexpressly understood, the operations above or below may or may not beimplemented in order. Conversely, the operations may be performed ininverted order, or simultaneously. Besides, one or more other operationsmay be added to the flowcharts, or one or more operations may be omittedfrom the flowchart.

An aspect of the present disclosure relates to systems and methods forsmoothly rendering and/or displaying an object box of an object in animage in accordance with the image. The systems may obtain a pluralityof images, in a temporal sequence, each of the plurality of imagesrelating to an object. The systems may also obtain a first processedimage by performing a smart algorithm analysis on a first image in theplurality of images, the smart algorithm analysis including identifyingthe object in the first image and determining a first coordinate of theobject in the first image. The systems may further determine a firstpixel distance between the object in two adjacent images in theplurality of images, and render the object box for the object in each ofthe plurality of images for display based on the first coordinate of theobject in the first image and the first pixel distance.

FIG. 1 is a schematic diagram illustrating an exemplary securityscreening system 100 according to some embodiments of the presentdisclosure. The security screening system 100 may capture an image of anobject and determine a coordinate of the object in the image. Thesecurity screening system 100 may further render an object boxsurrounding the object in the image and display the image and the objectbox together. As illustrated in FIG. 1, the security screening system100 may include a security screening equipment 110, an image processingdevice 120, a terminal 130, a storage device 140, a network 150, and/orany other suitable component in accordance with various embodiments ofthe disclosure.

The security screening equipment 110 may include an image capture deviceand a conveyor belt 113. The image capture device may be configured tocapture one or more images of an object 112 located on the conveyor belt113. In some embodiments, the conveyor belt 113 may move and the imagecapture device may capture a plurality of images of the object 112 in atemporal sequence. The image capture device may include an opticalcamera, an infrared camera, an X-ray imaging device, a ComputedTomography (CT) imaging device, a Magnetic Resonance (MR) imagingdevice, etc. The optical camera may be a static camera, a pan-tilt-zoomcamera, a moving camera, a stereoscopic camera, a structured lightcamera, a time-of-flight camera, etc. Specifically, the image capturedevice may be a camera equipped with a time-of-flight device, a Kinectsensor, a 3D laser scanner, a photographic scanner, etc. As used in thisapplication, an image may be a still image, a video, a stream video, ora video frame obtained from a video. The image may be a 2D image or a 3Dimage.

In some embodiments, the image capture device may be an X-ray imagingdevice including an X-ray generator 111 and an X-ray detector panel (notshown in FIG. 1). The X-ray generator 111 and an X-ray detector panelmay located on different side shell of the security screening equipment110 opposite to each other. For example, the X-ray generator 111 maylocated on a top shell of the security screening equipment 110, and theX-ray detector panel may be located on a shell opposite to the X-raygenerator 111, e.g., the X-ray detector panel may be located under theconveyor belt 113. The X-ray generator 111 may be configured to emitX-ray which go through the object 112 moving on the conveyor belt 113,and the X-ray detector panel including a plurality of X-ray detectorsmay be configured to detect the X-ray to obtain a plurality of images ina temporal sequence.

The plurality of images captured by the security screening equipment 110may be stored in the storage device 140, and/or sent to the imageprocessing device 120, or the terminal 130 via the network 150.

The image processing device 120 may be configured to allow one or moreoperators (e.g., a security staff) to set parameters to control thesecurity screening equipment 110 and/or the image processing device 120.

The image processing device 120 may also be configured to process theimage captured by the security screening equipment 110 or retrieved fromanother component in the security screening system 100 (e.g., thestorage device 140, the terminal 130) to determine a processed image.For example, the image processing device 120 may identify an object inan image by perform smart algorithm analysis on the image and determinea coordinate of the object in the image. For another example, the imageprocessing device 120 may determine a pixel distance between the objectin two adjacent images of the plurality of images. Additionally, theimage processing device 120 may render an object box surrounding theidentified object and display the object box in accordance with theimage.

In some embodiments, the image processing device 120 may include one ormore processing engines (e.g., single-core processing engine(s) ormufti-core processor(s)). Merely by way of example, the image processingdevice 120 may include a central processing unit (CPU), anapplication-specific integrated circuit (ASIC), an application-specificinstruction-set processor (ASIP), a graphics processing unit (GPU), aphysics processing unit (PPU), a digital signal processor (DSP), a fieldprogrammable gate array (FPGA), a programmable logic device (PLD), acontroller, a microcontroller unit, a reduced instruction-set computer(RISC), a microprocessor, or the like, or any combination thereof.Details of the image processing device 120 may be described in thepresent disclosure. See, e.g., FIG. 4 and the descriptions thereof.

The terminal 130 may be connected to or communicate with the imageprocessing device 120 and/or the security screening equipment 110. Theterminal 130 may allow one or more operators (e.g., a security staff) tocontrol the production and/or display of the data (e.g., the imagecaptured by the security screening equipment 110) on a display. Forexample, the operator may set a speed of the conveyor belt via theterminal 130 and may pause the move of the conveyor belt. The operatormay set a first frame rate of the image captured by the securityscreening equipment 110. The operator may also set a second frame rateof the image processed by the image processing device 120 by performinga smart algorithm analysis via the terminal 130. The terminal 130 mayinclude an input device, an output device, a control panel, a display(not shown in FIG. 1), or the like, or a combination thereof.

Exemplary input device may include a keyboard, a touch screen, a mouse,a remote controller, a wearable device, or the like, or a combinationthereof. For example, the input device may include alphanumeric andother keys that may be inputted via a keyboard, a touch screen (e.g.,with haptics or tactile feedback, etc.), a speech input, an eye trackinginput, a brain monitoring system, or any other comparable inputmechanism. The input information received through the input device maybe communicated to the image processing device 120 via the network 150for further processing. Exemplary input device may further include acursor control device, such as a mouse, a trackball, or cursor directionkeys to communicate direction information and command selections to, forexample, the image processing device 120 and to control cursor movementon the screen or another display device.

The storage device 140 may store data and/or instructions. The data mayinclude an image (e.g., an image obtained by the security screeningequipment 110), relevant information of the image, etc. In someembodiments, the storage device 140 may store data and/or instructionsthat the image processing device 120 may execute or use to performexemplary methods described in the present disclosure. In someembodiments, the storage device 140 may include a mass storage, aremovable storage, a volatile read-and-write memory, a read-only memory(ROM), or the like, or any combination thereof. Exemplary mass storagemay include a magnetic disk, an optical disk, a solid-state drive, etc.Exemplary removable storage may include a flash drive, a floppy disk, anoptical disk, a memory card, a zip disk, a magnetic tape, etc. Exemplaryvolatile read-and-write memory may include a random access memory (RAM).Exemplary RAM may include a dynamic RAM (DRAM), a double date ratesynchronous dynamic RAM (DDR SDRAM), a static RAM (SRAM), a thyristorRAM (T-RAM), and a zero-capacitor RAM (Z-RAM), etc. Exemplary ROM mayinclude a mask ROM (MROM), a programmable ROM (PROM), an erasableprogrammable ROM (PEROM), an electrically erasable programmable ROM(EEPROM), a compact disk ROM (CD-ROM), and a digital versatile disk ROM,etc. In some embodiments, the storage device 140 may be implemented on acloud platform, Merely by way of example, the cloud platform may includea private cloud, a public cloud, a hybrid cloud, a community cloud, adistributed cloud, an inter-cloud, a multi-cloud, or the like, or anycombination thereof.

The network 150 may facilitate communications between various componentsof the security screening system 100. The network 150 may be a singlenetwork, or a combination of various networks. Merely by way of example,the network 150 may include a cable network, a wireline network, anoptical fiber network, a tele communications network, an intranet, anInternet, a local area network (LAN), a wide area network (WAN), awireless local area network (WLAN), a metropolitan area network (MAN), awide area network (WAN), a public telephone switched network (PSTN), aBluetooth™ network, a ZigBee™ network, a near field communication (NFC)network, a global system for mobile communications (GSM) network, acode-division multiple access (CDMA) network, a time-division multipleaccess (TDMA) network, a general packet radio service (CPRS) network, anenhanced data rate for GSM evolution (EDGE) network, a wideband codedivision multiple access (WCDMA) network, a high speed downlink packetaccess (HSDPA) network, a long term evolution (LTE) network, a userdatagram protocol (UDP) network, a transmission controlprotocol/Internet protocol (TCP/IP) network, a short message service(SMS) network, a wireless application protocol (WAP) network, a ultrawide band (UWB) network, an infrared ray, or the like, or anycombination thereof. The network 150 may also include various networkaccess points, e.g., wired or wireless access points such as one or morebase stations or Internet exchange points through which a data sourcemay connect to the network 150 in order to transmit information via thenetwork 150.

It should be noted that the descriptions above in relation to thesecurity screening system 100 is provided for the purposes ofillustration, and not intended to limit the scope of the presentdisclosure. For persons having ordinary skills in the art, variousvariations and modifications may be conducted under the guidance of thepresent disclosure. However, those variations and modifications do notdepart the scope of the present disclosure. For example, part or all ofthe images generated by the security screening equipment 110 may beprocessed by the terminal 130. As another example, the securityscreening equipment 110 and the image processing device 120 may beimplemented in one single device configured to perform the functions ofthe security screening equipment 110 and the image processing device 120described in this disclosure. As still another example, the terminal130, and the storage device 140 may be combined with or part of theimage processing device 120 as a single device. Similar modificationsshould fall within the scope of the present disclosure.

FIG. 2 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary computing device according to someembodiments of the present disclosure. The image processing device 120and/or the terminal 130 may be implemented using one or more computingdevices 200 and/or one or more portions of computing devices 200.

The computing device 200 may be used to implement any part of the datatransmission as described herein. For example, the image processingdevice 120 may be implemented on the computing device 200, via itshardware, software program, firmware, or a combination thereof. Althoughonly one such computing device 200 is shown, for convenience, thecomputing device 200 functions relating to the image processing asdescribed herein may be implemented in a distributed fashion on a numberof similar computing devices, to distribute the processing load.

The computing device 200, for example, may include COM ports 250connected to and from a network connected thereto to facilitate datacommunications. The computing device 200 may also include a processor220, in the form of one or more processors, for executing programinstructions. The computing device 200 may include an internalcommunication bus 210, a program storage and data storage of differentforms, such as, and a read only memory (ROM) 230, a random access memory(RAM) 240, or a disk 270, for various data files to be processed and/ortransmitted by the computing device 200. The computing device 200 mayalso include program instructions stored in the ROM 230, RAM 240, and/orany other type of non-transitory storage medium to be executed by theprocessor 220. The methods and/or processes of the present disclosuremay be implemented as the program instructions. The computing device 200may also include an I/O component 260, supporting input/output betweenthe computing device 200 and outside components. The computing device200 may also receive programming and data via network communications.

The processor 220 may execute instructions and/or data to perform one ormore functions described in the present disclosure. For example, theprocessor 220 may perform smart algorithm analysis on an image. In someembodiments, the processor 220 may include one or more processors (e.g.,single-core processor(s) or multi-core processor(s)). Merely by way ofexample, the processor 220 may include a central processing unit (CPU),an application-specific integrated circuit (ASIC), anapplication-specific instruction-set processor (ASIP), a graphicsprocessing unit (GPU), a physics processing unit (PPU), a digital signalprocessor (DSP), a field programmable gate array (FPGA), a programmablelogic device (PLD), a controller, a microcontroller unit, a reducedinstruction-set computer (RISC), a microprocessor, or the like, or anycombination thereof.

Merely for illustration, only one processor 220 is described in thecomputing device 200. However, it should be noted that the computingdevice 200 in the present disclosure may also include multipleprocessors, thus operations and/or method steps that are performed byone processor 220 as described in the present disclosure may also bejointly or separately performed by the multiple CPUs/processors. Forexample, if in the present disclosure the processor 220 of the computingdevice 200 executes both step A and step B, it should be understood thatstep A and step B may also be performed by two different CPUs/processorsjointly or separately in the computing device 200 (e.g., the firstprocessor executes step A and the second processor executes step B, orthe first and second processors jointly execute steps A and B).

The ROM 230, the RAM 240, and/or the disk 270 may store data and/orinstructions that may perform one or more functions described in thepresent disclosure. For example, the ROM 230, the RAM 240, and/or thedisk 270 may store instructions executed by the processor 220 todetermine a coordinate of the object 112 in the image. As anotherexample, the ROM 230, the RAM 240, and/or the disk 270 may storeinstructions executed by the processor 220 to render an object box. Insome embodiments, the RAM 240 may include a dynamic RAM (DRAM), a doubledate rate synchronous dynamic RAM (DDR SDRAM), a static RAM (SRAM), athyristor RAM (T-RAM), and a zero-capacitor RAM (Z-RAM), or the like, orany combination thereof. In some embodiments, the ROM 230 may include amask ROM (MROM), a programmable ROM (PROM), an erasable programmable ROM(EPROM), an electrically-erasable programmable ROM (EEPROM), a compactdisk ROM (CD-ROM), and a digital versatile disk ROM, or the like, or anycombination thereof. In some embodiments, the disk 270 may include amagnetic disk, an optical disk, a solid-state drive, a flash drive, afloppy disk, an optical disk, a memory card, a zip disk, a magnetictape, or the like, or any combination thereof. In some embodiments, theROM 230, the RAM 240, and/or the disk 270 may include a data storage, anapplication, etc. In some embodiments, the data storage may be anyhardware or software for storing data, including a circuitry, a program,etc. In some embodiments, the application may include any applicationthat may be installed in the computing device 200 for querying data.

The I/O 260 may support an input/output between the computing device 200and an outside component. Merely by way of example, the I/O 260 mayinclude a display, a keypad/keyboard, or the like, or any combinationthereof. The display may be an output device for presenting informationin visual form. In some embodiments, the display may include a liquidcrystal display (LCD) panel, a light emitting diode display (LED) panel,an organic light emitting diodes (OLED) panel, a cathode ray tube (CRT)display, a plasma display, a touchscreen, a simulated touchscreen, thelike, or any combination thereof. The keypad/keyboard may be an inputdevice for typing in information from a user. In some embodiments, thekeypad/keyboard may include a standard alphanumeric keyboard, asimplified alphanumeric keyboard, a flexible keyboard, a handheldkeyboard, a software keyboard, an on-screen keyboard, a laser projectionkeyboard, a sense board, or the like, or any combination thereof.

The COM ports 250 may be connected to a network to facilitate datacommunications. In some embodiments, the COM ports 250 may be aninterface with the network 150 and/or one or more components in thesecurity screening system 100. In some embodiments, the COM ports 250may be any type of wired or wireless network interface. Merely by way ofexample, the COM ports 250 may include a cable network interface, awireline network interface, an optical fiber network interface, atelecommunications network interface, an intranet interface, an internetinterface, a local area network (LAN) interface, a wide area network(WAN) interface, a wireless local area network (WLAN) interface, ametropolitan area network (MAN) interface, a wide area network (WAN)interface, a public telephone switched network (PSTN) interface, aBluetooth network interface, a ZigBee network interface, a near fieldcommunication (NFC) network interface, or the like, or any combinationthereof. In some embodiments, the COM ports 250 may be implementedaccording to programming and/or computer language(s). The COM ports 250may include circuitry for coupling the computing device 200 to one ormore networks, and is constructed for use with one or more communicationprotocols and technologies including, global system for mobilecommunications (GSM), code-division multiple access (CDMA),time-division multiple access (TDMA), general packet radio service(CPRS), enhanced data rate for GSM evolution (EDGE), wideband codedivision multiple access (WCDMA), high speed downlink packet access(HSDPA), long term evolution (LTE), user datagram protocol (UDP),transmission control protocol/Internet protocol (TCP/IP), short messageservice (SMS), wireless application protocol (WAP), ultra wide band(UWB), IEEE 802.16 worldwide interoperability for microwave access(WiMax), session initiated protocol/real-time transport protocol(SIP/RTP), or any of a variety of other wireless communicationprotocols.

The internal communication bus 210 may transfer information and/or databetween one or more components of the computing device 200. For example,the internal communication bus 210 may connect the processor 220 with astorage (e.g., the RAM 240, the ROM 230, etc.) for exchanginginformation and/or data. In some embodiments, the internal communicationbus 210 may include a hardware component and/or a softwareimplementation. For example, the internal communication bus 210 mayinclude a wire, an optical fiber, a cable, a communication protocol, orthe like, or any combination thereof.

FIG. 3 is a schematic diagram illustrating exemplary components of anexemplary user device according to some embodiments of the presentdisclosure. As illustrated in FIG. 3, the user device 300 may include acommunication module 320, a display 310, a graphic processing unit (GPU)330, a central processing unit (CPU) 340, an I/O port 350, a memory 360,and a storage 390. In some embodiments, any other suitable component,including but not limited to a system bus or a controller (not shown),may also be included in the user device 300. In some embodiments, amobile operating system 370 (e.g., iOS™, Android™, Windows Phone™) andone or more applications 380 may be loaded into the memory 360 from thestorage 390 in order to be executed by the processor 340. The userdevice 300 may be an embodiment of the terminal 130. The applications380 may include an image player for receiving and displaying an imageprovided by the security screening equipment 110 and/or the imageprocessing device 120 through the network 150.

To implement various modules, units, and their functionalities describedin the present disclosure, computer hardware platforms may be used asthe hardware platform(s) for one or more of the elements describedherein. A computer with user interface elements may be used to implementa personal computer (PC) or any other type of work station or terminaldevice. A computer may also act as a server if appropriately programmed.

FIG. 4 is a block diagram of an exemplary image processing device 120according to some embodiments of the present disclosure. The imageprocessing device 120 may include an input module 410, a processingmodule 420, a display module 430, and a storage module 440.

The input module 410 may be configured to receive information input by auser. In some embodiments, the information received by the input module410 may be stored in the storage module 440, and/or be transmitted tothe processing module 420 for further processing. In some embodiments,the information received by the input module 410 may include controllinginstructions, and may be transmitted to the security screening equipment110 to control the operation of the image capture device and themovement of the conveyor belt 113. For example, the information receivedby the input module 410 may include the imaging range of the imagecapture device of the security screening equipment 110, the size of theX-ray detector panel, the size of the image captured by the securityscreening equipment 110, etc. For another example, the input module 410may be configured to receive parameters to set the speed of the conveyorbelt 113, pause the movement of the conveyor belt 113, set a first framerate of the image captured by the security screening equipment 110, andset a second frame rate of the image processed by the image processingdevice 120. As used herein, the first frame rate of the image capturedby the security screening equipment 110 may represent an image capturingfrequency. For example, the first frame rate of the image captured bythe security screening equipment 110 may be set to 60 frames per second(fps), which represents that the security screening equipment 110captures 60 images per second, i.e., the security screening equipment110 captures or generates one image every 16.67 ms in a temporalsequence. The second frame rate of the image processed by the imageprocessing device 120 may represent an image processing frequency. Forexample, the second frame rate of the image processed by the imageprocessing device 120 may be set to 15 fps, which represents that theimage processing device 120 acquires and processes 15 images per second.In some embodiments, the operator may set the first frame rate to 60fps, which represents that the security screening equipment 110 capturesan image every 16.67 ms, and set the second frame rate to 15 fps, whichrepresents that the image processing device 120 processes an image every66.67 ms, thus the image processing device 120 may acquire one image forevery four images to process. For example, the security screeningequipment 110 may capture a plurality of images in a temporal sequenceevery 16.67 ms, and the image processing device 120 may acquire thefirst generated image, the fifth generated image, the ninth generatedimage, . . . of the plurality images in the temporal sequence forfurther processing.

The processing module 420 may be configured to process image. In someembodiments, the processing module 420 may obtain an image from thesecurity screening equipment 110, the terminal 130, the storage device140, or the storage module 440. In some embodiments, the processingmodule 420 may identify an object in the image by perform smartalgorithm analysis on the image and determine a coordinate of the objectin the image. In some embodiments, the processing module 420 maydetermine a pixel distance between the object in two adjacent images ofthe plurality of images. Additionally, the processing module 420 mayrender an object box surrounding the identified object in the image.Details of the processing module 420 may be described in the presentdisclosure. See, e.g., FIG. 5 and the descriptions thereof.

The display module 430 may be configured to display information. Theinformation may include data before and/or after image processing, arequest for input or parameter relating to image capturing and/orprocessing. In some embodiments, the information may also includeinstructions using to prompt user to perform an input or other controloperations. The display module 430 may be configured to display an imagecaptured by the security screening equipment 110 and an object boxrendered by the processing module 420 simultaneously. Exemplary displaymodule 430 may include a liquid crystal display (LCD), a light emittingdiode (LED)-based display, a flat panel display or curved screen (ortelevision), a cathode ray tube (CRT), or the like, or a combinationthereof.

The storage module 440 may be configured to store information and/ordata received from the input module 410, image data generated by thesecurity screening equipment 110, processed data by the processingmodule 420, or the like, or any combination thereof. In someembodiments, the storage module 440 may include a mass storage,removable storage, a volatile read-and-write memory, a read-only memory(ROM), or the like, or any combination thereof. The mass storage mayinclude a magnetic disk, an optical disk, a solid-state drive, etc. Theremovable storage may include a flash drive, an optical disk, a memorycard, a zip disk, a magnetic tape, etc. The volatile read-and-writememory may include a random access memory (RAM). The RAM may include adynamic RAM (DRAM), a double date rate synchronous dynamic RAM (DDRSDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM), and azero-capacitor RAM (Z-RAM), etc. The ROM may include a mask ROM (MROM),a programmable ROM (PROM), an erasable programmable ROM (EPROM), anelectrically erasable programmable ROM (EEPROM), a compact disk ROM(CD-ROM), and a digital versatile disk ROM, etc. In some embodiments,the storage module 440 may store one or more programs and/orinstructions that may be executed by the processor 220 of the imageprocessing device 120 (e.g., the processing module 420) to performexemplary methods and/or processes described in the disclosure. Forexample, the storage module 440 may store programs and/or instructionsexecuted by the processor 220 of the image processing device 120 toobtain image data, determine a plurality of images in a temporalsequence, determine a processed image by performing smart algorithmanalysis, rendering an object box in each of the plurality of images.

Modules of the image processing device 120 may be connected to orcommunicate with each other via a wired connection or a wirelessconnection. The wired connection may include a metal cable, an opticalcable, a hybrid cable, or the like, or any combination thereof. Thewireless connection may include a Local Area Network (LAN), a Wide AreaNetwork (WAN), a Bluetooth, a ZigBee, a Near Field Communication (NFC),or the like, or any combination thereof. Two or more of the modules maybe combined into a single module, and any one of the modules may bedivided into two or more units. For example, the input module 410 andthe display module 430 may be combined into a single module (e.g., atouch screen) that may be configured to receive information and/or datainput by a user, and display the image and the object box.

FIG. 5 is a block diagram illustrating an exemplary processing moduleaccording to some embodiments of the present disclosure. As illustratedin FIG. 5, the processing module 420 may include an acquisition unit510, an analysis unit 520, a distance determination unit 530, and arendering unit 540.

The acquisition unit 510 may obtain one or more images from othercomponents of the security screening system 100. In some embodiments,the acquisition unit 510 may obtain a plurality of images from thesecurity screening equipment 110, the storage device 140, the terminal130, or the storage module 440. The plurality of images may be capturedby the security screening equipment 110 in a temporal sequence accordingto a first frame rate. The first frame rate may be set by a user. Forexample, the first frame rate of the image captured by the securityscreening equipment 110 may be set to 60 fps, which represents that thesecurity screening equipment 110 captures 60 images per second, i.e.,the security screening equipment 110 captures an image every 16.67 ms ina temporal sequence. The acquisition unit 510 may further acquire partof the plurality of images according to a second frame rate. The secondframe rate may also be set by a user. For example, the second frame ratemay be set to 15 fps, which represents that acquisition unit 510acquires 15 images per second for further processing. In someembodiments, the user may set the first frame rate to 60 fps and set thesecond frame rate to 15 fps, which represents that the securityscreening equipment 110 captures an image every 16.67 ms, and theacquisition unit 510 acquires one image for every four images forfurther processing. For example, the security screening equipment 110may capture a plurality of images in a temporal sequence every 16.67 ms,and the acquisition unit 510 may acquire the first generated image, thefifth generated image, the ninth generated image, . . . of the pluralityimages in the temporal sequence for further processing.

The analysis unit 520 may be configured to process an image to determinea processed image. The image may be acquired by the acquisition unit 510according to the second frame rate. The processing of the image mayinclude identifying an object in the image by performing smart algorithmanalysis on the image and determining a coordinate of the object in theimage. In some embodiments, the processing of the image may furtherinclude determining that a contour of the identified object isincomplete and completing the contour of the identified object. Forexample, there may be two objects partially overlapping in a luggage,and a contour of a target object may be incomplete in the image due tothe coverage of the other object. The analysis unit 520 may alsoidentify the target object by analyzing the image using, for example, anintelligent algorithm. The smart algorithm analysis may be used toidentify an object in the image. The smart algorithm analysis mayinclude image identification algorithm such as a convolutional neutralnetwork (CNN), Region-based Convolutional Network (R-CNN), SpatialPyramid Pooling Network (SPP-Net), Fast Region-based ConvolutionalNetwork (Fast R-CNN), Faster Region-based Convolutional Network (FasterR-CNN), or the like, or any combination thereof. The processed image mayinclude data of the corresponding image and information relating to theobject. The information relating to the object may include one or morepixels of the object and the coordinate of the object in the image. Forexample, the image acquired by the acquisition unit 510 from thesecurity screening equipment 110 may be as shown in FIG. 11A. Theanalysis unit 520 may perform the smart algorithm analysis on the imageto identify an object 112 (e.g., a knife) in the image. The analysisunit 520 may further determine a coordinate of the object 112 in theimage. Thus, the analysis unit 520 may determine the processed imageincluding the corresponding image and the information relating to theobject, as shown in FIG. 11B. As used herein, the coordinate of theobject 112 may include a coordinate of a pixel of the object 112 in theimage, or a coordinate of a range including the one or more pixels ofthe object 112 in the image.

For example, the image may include pixel array consisting of a pluralityof pixels, such as 1920*1080 (pixel array including pixels of 1920columns and 1080 rows). The analysis unit 520 may identify the object112 by performing the smart algorithm analysis on the image. Theanalysis unit 520 may determine the one or more pixels of the object 112in the image. The analysis unit 520 may then determine a coordinatesystem in the image in order to determine the coordinate of the object112. The analysis unit 520 may determine a pixel of the image as theorigin of the coordinate system, e.g., pixel O of the first column andthe last row of the image, as shown in FIG. 11B. The last row of theimage may be the X-axis and the first column of the image may be theY-axis. Therefore the analysis unit 520 may determine a coordinate ofany pixel in the image using a column number count and a row numbercount. In some embodiments, the analysis unit 520 may determine a centerpixel of the image as the origin of the coordinate system. Furthermore,the image processing device 120 may determine any pixel as the origin ofthe coordinate system.

In some embodiments, the coordinate of the object 112 may include acoordinate of a pixel of the object 112 in the image. The pixel mayinclude a center pixel A of the object, a vertex pixel of the object112, or any pixel of the object 112. The analysis unit 520 may determinethe coordinate of pixel A (X_(A), Y_(A)) as the coordinate of the object112, wherein X_(A) represents the column number count of the pixel A inthe pixel array, and Y_(A) represents the row number count of the pixelA in the pixel array.

In some embodiments, the coordinate of the object 112 may be acoordinate of a range including the one or more pixels of the object 112in the image. The range may include a rectangle region, a square region,a circle region, an oval region, a triangle region, an irregular region,or the like, or any combination thereof. In some embodiments, the rangemay be a rectangle region. The analysis unit 520 may identify the objectincluding the one or more pixels in the image, and determine acoordinate for each of the plurality of pixels. The analysis unit 520may determine, among the coordinates of the one or more pixels, amaximum X-axis value X_(max), a maximum Y-axis value Y_(max), a minimumX-axis value X_(min), and a minimum Y-axis value Y_(min). The analysisunit 520 may determine a rectangle region defined by (X_(min), X_(max),Y_(min), Y_(max)) as the coordinate of the object 112. The rectangleregion defined by (X_(min), X_(max), Y_(min), Y_(max)) may be surroundedby a left column of X_(min), a right column of X_(max), a top row ofY_(max), and a bottom row of Y_(min). In some embodiments, the range maybe a circle region. The analysis unit 520 may determine the coordinateof pixel A (X_(A), Y_(A)) as the center of the circle region, anddetermine a maximum distance D_(max) among distances between the pixel Aand any other pixels of the one or more pixels of the object as theradius of the circle region. Accordingly, the analysis unit 520 maydetermine the circle region defined by center point (X_(A), Y_(A)) andthe radius r=D_(max) as the coordinate of the object 112. In someembodiments, the range may be a square region. The analysis unit 520 maydetermine the square region defined by the center point (X_(A), Y_(A))and side-length L=2*D_(max) as the coordinate of the object 112.

In some embodiments, the image captured by the security screeningequipment 110 may be a 3D image including a plurality of voxels. Theanalysis unit 520 may determine a coordinate system in the 3D image anddetermine a coordinate (X, Y, Z) of each voxel, similarly to the 2Dimage. The coordinate of the object 112 in the 3D image may include acoordinate of a voxel of the object in the image, or a coordinate of arange including one or more voxels of the object in the image. The rangemay include a cuboid region, a cube region, a sphere region, anellipsoid region, a pyramid region, an irregular region, or the like, orany combination thereof. Similar to 2D images, the coordinate of theobject 112 may also be determined by the analysis unit 520 using valuesof X-axis, Y-axis, and Z-axis of the coordinate system.

The distance determination unit 530 may be configured to determine apixel distance between the object in two adjacent images. As usedherein, the two adjacent images may be two images captured by thesecurity screening equipment 110 at two adjacent time points, and thepixel distance between the object in the two adjacent images may be adistance between a first coordinate of the object in a first image ofthe two adjacent images and a second coordinate of the object in asecond image of the two adjacent images. In some embodiments, the pixeldistance may be a number count of pixels of the image along one or moredirections. The one or more directions may include a first directionparallel to the X-axis of the coordinate system or a second directionparallel to the Y-axis. In some embodiments, the first directionparallel to the X-axis may be the moving direction of the conveyor belt113. For example, if the first coordinate of the object in the firstimage of the two adjacent images is (100, 100), and the secondcoordinate of the object in the second image of the two adjacent imagesis (80, 100), the distance determination unit 530 may determine that thepixel distance between the object in the two adjacent images is 20 alongthe first direction parallel to the X-axis. Details of the determinationof the pixel distance between the object in two adjacent images of theplurality of images may be described in the present disclosure.

The rendering unit 540 may be configured to render an object box in theimage. The object box may surround the object identified by the analysisunit 520. The object box may be displayed on a screen in accordance withthe image in order to draw attention of a security staff. The object maybe a knife, a gun, a cigarette lighter, or the like, and may havevarious shapes. In some embodiments, the object box may have anirregular shape, for example, a shape of a contour of the identifiedobject. In some embodiments, the object box may have a shape ofrectangle, square, triangle, circle, oval, or the like, or anycombination thereof. The rendering unit 540 may render the object boxusing a color different from the image for alert, e.g., red, green,yellow, etc. The rendering unit 540 may further render the object box ina twinkling manner. Details of the rendering may be described in thepresent disclosure.

In some embodiments, the rendering unit 540 renders the object box basedon the coordinate of the object 112 in the image. The object box 1201may surround the object 112, as shown in FIG. 12. In some embodiments,the object box 1201 may include sides of the range representing thecoordinate of the object 112. In some embodiments, the object box 1201may surround a region larger than the range representing the coordinateof the object 112. For example, the range representing the coordinate ofthe object 112 may be a rectangle region defined by (X_(min), X_(max),Y_(min), Y_(max)), and the object box 1201 may be a rectangle defined by(X_(min)−N_(x1), X_(max)+N_(x2), Y_(min)−N_(Y1), Y_(max)+N_(Y2)),wherein N_(x1), N_(x2), N_(Y1), N_(Y2) may be any integer respectively.For example, the range representing the coordinate of the object 112 maybe a square region defined by center point (X_(A), Y_(A)) andside-length L=2*D_(max), or a circle region defined by center point(X_(A), Y_(A)) and the radius D_(max), and the object box 1201 may be acircle defined by the center point (X_(A), Y_(A)) and a radiusD_(max)+D, wherein D may be any integer.

Units of the processing module 420 may be connected to or communicatewith each other via a wired connection or a wireless connection. Thewired connection may include a metal cable, an optical cable, a hybridcable, or the like, or any combination thereof, Two or more of the unitsmay be combined into a single unit, and any one of the units may bedivided into two or more sub-units. For example, the acquisition unit510 and the analysis unit 520 may be combined into a single unit thatmay be configured to acquire images and process the acquired images. Foranother example, the analysis unit 520 may be divided into twosub-units, one of the sub-unit may be configured to identify the objectin the image and the other sub-unit may be configured to determine acoordinate of the object.

FIG. 6 is a flow chart illustrating an exemplary process for renderingobject boxes according to some embodiments of the present disclosure.The process 600 may be executed by the security screening system 100.For example, the process 600 may be implemented as a set of instructionsstored in the storage ROM 230 or RAM 240. The processor 220 and/or themodules/units in FIGS. 4 and 5 may execute the set of instructions, andwhen executing the instructions, the processor 220 and/or themodules/units may be configured to perform the process 600. Theoperations of the illustrated process presented below are intended to beillustrative. In some embodiments, the process 600 may be accomplishedwith one or more additional operations not described and/or without oneor more of the operations discussed. Additionally, the order in whichthe operations of the process 600 as illustrated in FIG. 6 and describedbelow is not intended to be limiting.

In 601, the acquisition unit 510 may obtain a first plurality of images.In some embodiments, the first plurality of images may be captured bythe security screening equipment 110 in a temporal sequence. Forexample, when a luggage is located on the conveyor belt 113, the luggagemay be transmitted to an internal cavity of the security screeningequipment 110. When the luggage is moving on the conveyor belt 113, thesecurity screening equipment 110 may capture a video including the firstplurality of images according to a first frame rate set by a user. Insome embodiments, each of the first plurality of images may relate to anobject. For example, each of the first plurality of images may includeat least part of the object. In some embodiments, some, but not all, ofthe first plurality of images may relate to the object. In someembodiments, the phrase “the first plurality of images” refers to theimages that may relate to the object.

In some embodiments, the acquisition unit 510 may obtain the firstplurality of images from the security screening equipment 110 andtransmit the first plurality of images to a storage (e.g., the storagedevice 140, the terminal 130, or the storage module 440). In someembodiments, the acquisition unit 510 may obtain the first plurality ofimages from the storage (e.g., the storage device 140, the terminal 130,or the storage module 440).

In 602, the analysis unit 520 may determine a first processed image byperforming a smart algorithm analysis on a first image of the firstplurality of images. In some embodiments, the first plurality of imagesmay be captured in a temporal sequence. The first image of the firstplurality of images may be the first generated image, in a temporalsense, in the first plurality of images. In some embodiments, theanalysis unit 520 may perform smart algorithm analysis on the firstimage to identify the object in the first image. The object may includea knife, a gun, a cigarette lighter, or the like. In some embodiments,the smart algorithm analysis may include image identification algorithmsuch as a convolutional neutral network (CNN), Region-basedConvolutional Network (R-CNN), Spatial Pyramid Pooling Network(SPP-Net), Fast Region-based Convolutional Network (Fast R-CNN), FasterRegion-based Convolutional Network (Faster R-CNN). For example, theanalysis unit 520 may process the first image using a CNN model toidentify the object. The CNN model may be trained in advance. Theanalysis unit 520 may determine one or more pixels of the object in thefirst image, and determine a first coordinate of the object in the firstimage. The first processed image may include the first image andinformation relating to the object in the first image. The informationrelating to the object may include the one or more pixels of the objectand the first coordinate of the object in the first image.

For example, the first image can be FIG. 11A, which may include theobject 112, e.g., a knife. The analysis unit 520 may perform the smartalgorithm analysis on the first image to identify the object 112 in thefirst image and determine the one or more pixels of the object 112. Theanalysis unit 520 may further determine the first coordinate of theobject 112 in the first image. The analysis unit 520 may determine thefirst processed image including data of the first image, the object 112identified and the first coordinate of the object 112, as shown in FIG.11B.

In 603, the distance determination unit 530 may determine a first pixeldistance between the object in two adjacent images of the firstplurality of images. In some embodiments, the two adjacent images may betwo images captured by the security screening equipment 110 at twoadjacent time points. For example, the two adjacent images may includethe first image and the second image captured by the security screeningequipment 110 later than and next to the first image. The first pixeldistance between the object in the two adjacent images may be a distancebetween the first coordinate of the object in the first image and asecond coordinate of the object in the second image of the two adjacentimages. The first pixel distance may be a number count of pixels alongthe X-axis direction and/or along the Y-axis direction. In someembodiments, the X-axis direction may be parallel to the movingdirection of the object or the conveyor belt 113. In some embodiments,the first pixel distance between the object in any two adjacent imagesof the first plurality of images may be the same. In some embodiments,the first pixel distance between the object in two adjacent images ofthe first plurality of images may be different. Details of thedetermination of the first pixel distance between the object in twoadjacent images of the first plurality of images may be described in thepresent disclosure. See, e.g., FIG. 7 or FIG. 8 and the descriptionsthereof.

In 604, the rendering unit 540 may render an object box in the firstimage. The object box may surround the object identified by the analysisunit 520. In some embodiments, the rendering unit 540 may render theobject box in each of the first plurality of images. The object box maybe displayed in accordance with each of the first plurality of images.In some embodiments, the object box may be configured to remind a user(e.g., security staff) that there is a dangerous thing (the object) in aluggage.

In some embodiments, the rendering unit 540 may render the object box inthe first image based on the first coordinate of the object in the firstimage In some embodiments, the rendering unit 540 may render the objectbox in each of the first plurality of images based on the firstcoordinate of the object in the first image, the first pixel distance,and a number count of images between the image and the first image.Details of the rendering the object box in each of the first pluralityof images based on the first coordinate of the object in the first imageand the first pixel distance may be described in the present disclosure.See, e.g., FIG. 10 and the descriptions thereof.

In 605, the display module 430 may display object boxes in accordancewith the first plurality of images. In some embodiments, the displaymodule 430 may display each image of the first plurality of images andthe corresponding object box simultaneously. In some embodiments, thedisplay module 430 may display the first plurality of images and theobject box in each of the first plurality of images according to thefirst frame rate. For example, the display module 430 may display animage once a time and display 60 images per second.

In some embodiments, the security screening equipment 110 may beconfigured to capture a video including the first plurality of imagesaccording to the first frame rate, and the display module 430 configuredto display the video including the first plurality of images and thecorresponding object box in each of the first plurality of imagesaccording to the first frame rate. In some embodiments, the displaymodule 430 may display the first plurality of images and the object boxin each of the first plurality of images according to a third framerate. The third frame rate may be different with the first frame rate.For example, the third frame rate may larger than the first frame rateor less than the first frame rate.

It should be noted that the above description of process 600 is merelyprovided for the purposes of illustration, and not intended to beunderstood as the only embodiment. For persons having ordinary skills inthe art, various variations and modifications may be conduct under theteaching of some embodiments of the present disclosure. In someembodiments, some operations may be reduced or added. However, thosevariations and modifications may not depart from the protecting of someembodiments of the present disclosure. For example, one or more otheroptional operations (e.g., a storing operation) may be added in theprocess 600. In the storing operation, the image processing device 120may store information and/or data associated with the plurality ofimages, the processed image in a storage device (e.g., the storagedevice 140, the storage module 440, the storage 390) as describedelsewhere in the present disclosure.

FIG. 7 is a flow chart illustrating an exemplary process for determininga first pixel distance between an object in two adjacent imagesaccording to some embodiments of the present disclosure. The process 700may be executed by the security screening system 100. For example, theprocess 700 may be implemented as a set of instructions stored in thestorage ROM 230 or RAM 240. The processor 220 and/or the modules/unitsin FIGS. 4 and 5 may execute the set of instructions, and when executingthe instructions, the processor 220 and/or the modules/units may beconfigured to perform the process 700. The operations of the illustratedprocess presented below are intended to be illustrative. In someembodiments, the process 700 may be accomplished with one or moreadditional operations not described and/or without one or more of theoperations discussed. Additionally, the order in which the operations ofthe process 700 as illustrated in FIG. 7 and described below is notintended to be limiting.

In 701, the distance determination unit 530 may determine an imagingrange of the image capture device of the security screening equipment110 and a size of an image captured by the image capture device. Theimaging range of the image capture device may be a range that the imagecapture device can.

In some embodiments, the size of the image may be set by a user via theimage processing device 120 or the terminal 130. In some embodiments,the size of the image may be set by the security screening system 100automatically. The size of the image may include the number of pixels,which may indicate the resolution of the image. For example, the size ofthe image may be 640*480, 800*600, 1024*768, 1280*800, 960*540,1280*720, 1366*768, 1980*1080, etc.

In some embodiments, the imaging range of the image capture device maybe set by a user via the image processing device 120 or the terminal130. In some embodiments, the imaging range of the image capture devicemay be determined by the security screening system 100 automatically. Insome embodiments, the image capture device may be an optical camera oran infrared camera, and the imaging range may be determined based atleast on a focal length of lens and distance between the lens and theconveyor belt 113.

In some embodiments, the image capture device may be an X-ray imagingdevice including an X-ray generator 111 and an X-ray detector panel. Theimaging range of the image capture device may be the size of the X-raydetector panel. For example, the X-ray detector panel may be a rectanglehaving a length of long side of 1 m and a length of short side of 0.8 m,and the imaging range of the image capture device may be 1 m*0.8 m. Insome embodiments, the imaging range of the image capture device may bethe size of a region including X-ray detectors in the X-ray detectorpanel. For example, the X-ray detector panel may be a rectangle having alength of long side of 1 m and a length of short side of 0.8 m, and theregion including X-ray detectors in the X-ray detector panel may be arectangle having a length of long side of 0.9 m and a length of shortside of 0.7 m, and the imaging range of the image capture device may be0.9 m*0.7 m.

In some embodiments, the imaging range may be a rectangle, and the longside of the rectangle may along a moving direction of the object. Therow of the pixel array of the image, e.g., the X-axis direction, may beparallel to the moving direction of the object.

In 702, the distance determination unit 530 may determine a pixel sizeof the image based on the imaging range of the image capture device andthe size of the image. The pixel size may be an actual size representedby a pixel of the image. For example, the imaging range of the imagecapture device may be 1 m*0.8 m, and the size of image may be 800*1000,which means that the image consists of pixels of 800 rows and 1000columns. The distance determination unit 530 may determine the pixelsize as 1 mm*1 mm.

In 703, the distance determination unit 530 may determine a timeinterval between two adjacent images of a first plurality of imagesbased on a first frame rate set by a user. The first plurality of imagesmay be captured by the security screening equipment 110 in a temporalsequence. In some embodiments, the first frame rate may be 60 fps, whichmeans that the security screening equipment 110 may capture 60 imagesper second. The distance determination unit 530 may determine that thetime interval between the two adjacent images is 16.67 ms.

In 704, the distance determination unit 530 may determine a movingdistance of an object based on the time interval and a speed of theconveyor belt 113. In some embodiments, the object may be located on theconveyor belt 113 of the security screening equipment 110, and move inaccordance with the conveyor belt 113. The speed of the conveyor belt113 may be set by a user via the image processing device 120 or theterminal 130. For example, the speed of the conveyor belt 113 may be setto 1 m/s, and the distance determination unit 530 may determine themoving distance is 16.67 mm along the moving direction.

In 705, the distance determination unit 530 may determine the firstpixel distance between the object in two adjacent images of the firstplurality of images based on the moving distance and the pixel size. Forexample, the moving distance may be 16.67 mm and the pixel size may be 1mm*1 mm, then the distance determination unit 530 may determine thefirst pixel distance as 16.67 along the moving direction, e.g., theX-axis direction. The pixel distance between the object in two adjacentimages may indicate a pixel number count between the object in the twoadjacent images along the moving direction of the object, i.e., theX-axis direction.

It should be noted that the above description of process 700 is merelyprovided for the purposes of illustration, and not intended to beunderstood as the only embodiment. For persons having ordinary skills inthe art, various variations and modifications may be conduct under theteaching of some embodiments of the present disclosure. In someembodiments, some operations may be reduced or added. However, thosevariations and modifications may not depart from the protecting of someembodiments of the present disclosure. For example, one or more otheroptional operations (e.g., a storing operation) may be added in theprocess 700. In the storing operation, the image processing device 120may store information and/or data associated with the imaging range ofthe image capture device, the size of the image, the pixel size, themoving distance, the first pixel distance in a storage device (e.g., thestorage device 140, the storage module 440, the storage 390) asdescribed elsewhere in the present disclosure.

FIG. 8 is a flow chart illustrating another exemplary process fordetermining a first pixel distance between an object in two adjacentimages according to some embodiments of the present disclosure. Theprocess 800 may be executed by the security screening system 100. Forexample, the process 800 may be implemented as a set of instructionsstored in the storage ROM 230 or RAM 240. The processor 220 and/or themodules/units in FIGS. 4 and 5 may execute the set of instructions, andwhen executing the instructions, the processor 220 and/or themodules/units may be configured to perform the process 800. Theoperations of the illustrated process presented below are intended to beillustrative. In some embodiments, the process 800 may be accomplishedwith one or more additional operations not described and/or without oneor more of the operations discussed. Additionally, the order in whichthe operations of the process 800 as illustrated in FIG. 8 and describedbelow is not intended to be limiting.

In 801, the acquisition unit 510 may obtain a first plurality of imagesand acquire two images from the first plurality of images. In someembodiments, the first plurality of images may be captured by thesecurity screening equipment 110 in a temporal sequence. For example,when a luggage is located on the conveyor belt 113, the luggage may betransmitted to an internal cavity of the security screening equipment110. When the luggage is moving on the conveyor belt 113, the securityscreening equipment 110 may capture a video including the firstplurality of images according to a first frame rate set by a user. Insome embodiments, each of the first plurality of images may relating toan object. For example, each of the first plurality of images mayinclude at least part of the object.

In some embodiments, the acquisition unit 510 may obtain the firstplurality of images from the security screening equipment 110 andtransmit the first plurality of images to a storage (e.g., the storagedevice 140, the terminal 130, or the storage module 440). In someembodiments, the acquisition unit 510 may obtain the first plurality ofimages from the storage (e.g., the storage device 140, the terminal 130,or the storage module 440).

The two images acquired by the acquisition unit 510 from the firstplurality of images may be any images of the first plurality of images.In some embodiments, the two images may be generated in adjacent timepoints. For example, the two images may include the first generatedimage and the second generated image, or the two images may include thethird generated image and the fourth generated image. In someembodiments, the two images may be not generated in adjacent timepoints, e.g., there may be one or more images generated between the twoimages. For example, the two images may include the first generatedimage and the fifth generated image, and there may be three images(e.g., the second generated image, the third generated image, the fourthgenerated image) between the two images. For another example, the twoimages may include the second generated image and the fourth generatedimage, and there may be one image (e.g., the third generated image)between the two images.

In some embodiments, the acquisition unit 510 may acquire the two imagesfrom the first plurality of images based on a user input via the imageprocessing device 120 or the terminal 130. The user may set a numbercount of the one or more images generated between the two images in thefirst plurality of images in the temporal sequence. For example, theuser may set the number count of the one or more images generatedbetween the two images in the first plurality of images in the temporalsequence to be 2, and the acquisition unit 510 may acquire the firstgenerated image and the fourth generated image of the first plurality ofimages.

In some embodiments, the acquisition unit 510 may acquire the two imagesbased on a first frame rate of the image captured by the securityscreening equipment 110 and a second frame rate. The first frame rateand the second frame rate may be set by a user via the image processingdevice 120 or the terminal 130. In some embodiments, the user may setthe first frame rate to 60 fps, and set the second frame rate to 15 fps,thus the acquisition unit 510 may acquire one image for every fourimages of the plurality of images. For example, the security screeningequipment 110 may capture the first plurality of images in a temporalsequence every 16.67 ms, and the acquisition unit 510 may acquire thetwo images including the first generated image and the fifth generatedimage of the first plurality images in the temporal sequence.

In 802, the analysis unit 520 may determine two processed images basedon the two images acquired by the acquisition unit 510. The twoprocessed images including a first processed image determined and asecond processed image. The analysis unit 520 may determine the firstprocessed image by performing smart algorithm analysis on a first imageof the two images, and determine the second processed image byperforming the smart algorithm analysis on a second image of the twoimages. As used herein, the first image of the two images may begenerated earlier than the second image. As described in the presentdisclosure, the first plurality of images may be captured in a temporalsequence according to a first frame rate. Details of generating aprocessed image may be described elsewhere in the present disclosure.See, e.g., FIG. 6 and the descriptions thereof. The processed image mayinclude the image and information relating to the object in the image.The information relating to the object may include the one or morepixels of the object and the coordinate of the object in the image.

For example, as illustrated in FIG. 13, five temporal images of thefirst plurality of images are overlapping with each other. The object112-A is in the first generated image of the five temporal images; theobject 112-B is in the second generated image of the five temporalimages; the object 112-C is in the third generated image of the fivetemporal images; the object 112-D is in the fourth generated image ofthe five temporal images; the object 112-E is in the fifth generatedimage of the five temporal images. It should be noted that the objects112-A, 112-B, 112-C, 112-D, 112-E is the same object 112, and thecoordinate of the object 112 in each of the five temporal images may bedifferent from each other due to movement of the object 112 located onthe conveyor belt 113. The acquisition unit 510 may acquire the twoimages including the first generated image including the object 112-A ofthe five temporal images and the fifth generated image including theobject 112-E of the five temporal images. The first processed image mayinclude the first coordinate of the object 112-A in the first image(i.e., the first generated image of the five temporal images), and thesecond processed image may include the second coordinate of the object112-E in the second image (i.e., the fifth generated image of the fivetemporal images).

In 803, the distance determination unit 530 may determine a second pixeldistance between the object in the first image of the two images and theobject in the second image of the two images. In some embodiments, asshown in FIG. 13, the first processed image may include the firstcoordinate of the object 112-A in the first image (i.e., the firstgenerated image of the five temporal images), and the second processedimage may include the second coordinate of the object 112-E in thesecond image (i.e., the fifth image of the five temporal images). Thedistance determination unit 530 may determine the second pixel distancebased on the first coordinate of the object in the first image and thesecond coordinate of the object in the second image. For example, asillustrated in FIG. 13, the distance determination unit 530 maydetermine the second pixel distance D_(p2).

In some embodiments, the second pixel distance may be a number count ofpixels along one or more directions. In some embodiments, the one ormore directions may include an X-axis direction parallel to the movingdirection of the object or the conveyor belt 113. In some embodiments,the one or more directions may include a Y-axis direction perpendicularto the moving direction of the object or the conveyor belt 113. Forexample, if the first coordinate of the object in the first image is(200, 100), and the second coordinate of the object in the second imageis (120, 100), the distance determination unit 530 may determine thatthe second pixel distance is 80 pixels along the X-axis directionparallel to the moving direction of the object. For another example, ifthe first coordinate of the object in the first image is (200, 100), andthe second coordinate of the object in the second image is (120, 96),the distance determination unit 530 may determine that the second pixeldistance is 80 pixels along the X-axis direction parallel to the movingdirection of the object and 4 pixels along the Y-axis directionperpendicular to the moving direction of the object.

In 804, the distance determination unit 530 may determine a number countof images between the first image and the second image of the twoimages. In some embodiments, the number count of the images between thefirst image and the second image of the two images may be set by theuser in 801. For example, the number count of images between the twoimages may be 0, 1, 2, . . . etc. In some embodiments, the number countof the images between the first image and the second image of the twoimages may be determined by the distance determination unit 530 based onthe first frame rate and the second frame rate. For example, if thefirst frame rate is 60 fps, and the second frame rate is 15 fps, thedistance determination unit 530 may determine that the number count ofthe one or more images between the first image and the second image ofthe two images is 3.

In 805, the distance determination unit 530 may determine the firstpixel distance between the object in two adjacent images of the firstplurality of images based on the second pixel distance and the numbercount of the one or more images between the two images. In someembodiments, the first pixel distance between the object in any twoadjacent images of the plurality of images may be the same. The distancedetermination unit 530 may determine the first pixel distance based onthe formula below:

$\begin{matrix}{{D_{p\; 1} = \frac{D_{p\; 2}}{n + 1}},} & (1)\end{matrix}$

where D_(p1) is the first pixel distance between the object in twoadjacent images of the first plurality of images, D_(p2) is the secondpixel distance between the object in the first image and the object inthe second image of the two images, n is the number count of the one ormore images between the two images. For example, if the number count ofthe one or more images between the two images is 0, e.g., the two imagesmay be generated in adjacent time points, then the distancedetermination unit 530 may determine that the first pixel distance isthe same with the second pixel distance.

It should be noted that the above description of process 800 is merelyprovided for the purposes of illustration, and not intended to beunderstood as the only embodiment. For persons having ordinary skills inthe art, various variations and modifications may be conduct under theteaching of some embodiments of the present disclosure. In someembodiments, some operations may be reduced or added. However, thosevariations and modifications may not depart from the protecting of someembodiments of the present disclosure. For example, one or more otheroptional operations (e.g., a storing operation) may be added in theprocess 800. In the storing operation, the image processing device 120may store information and/or data associated with the first pixeldistance, or the second pixel distance in a storage device (e.g., thestorage device 140, the storage module 440, the storage 390) asdescribed elsewhere in the present disclosure.

FIG. 9 is a flow chart illustrating another exemplary process forrendering object boxes according to some embodiments of the presentdisclosure. The process 900 may be executed by the security screeningsystem 100. For example, the process 900 may be implemented as a set ofinstructions stored in the storage ROM 230 or RAM 240. The processor 220and/or the modules/units in FIGS. 4 and 5 may execute the set ofinstructions, and when executing the instructions, the processor 220and/or the modules/units may be configured to perform the process 900.The operations of the illustrated process presented below are intendedto be illustrative. In some embodiments, the process 900 may beaccomplished with one or more additional operations not described and/orwithout one or more of the operations discussed, Additionally, the orderin which the operations of the process 900 as illustrated in FIG. 9 anddescribed below is not intended to be limiting.

In 901, the acquisition unit 510 may obtain a first plurality of imagesand acquire a second plurality of images from the first plurality ofimages. In some embodiments, the first plurality of images may becaptured by the security screening equipment 110 in a temporal sequenceaccording to a first frame rate. For example, when a luggage is locatedon the conveyor belt 113, the luggage may be transmitted to an internalcavity of the security screening equipment 110. When the luggage ismoving on the conveyor belt 113, the security screening equipment 110may capture a video including the first plurality of images according tothe first frame rate set by a user. In some embodiments, each of thefirst plurality of images may relating to an object. For example, eachof the first plurality of images may include at least part of theobject.

In some embodiments, the acquisition unit 510 may acquire the secondplurality of images from the first plurality of images according to asecond frame rate set by the user. The second plurality of images mayinclude at least two images. The acquisition unit 510 may also acquirethe second plurality of images from the first plurality of images in atemporal sequence.

In some embodiments, the first image of the second plurality of imagesmay be the first image of the plurality of image. For example, if thefirst frame rate is set to be 60 fps, and the second frame rate is setto be 15 fps, the security screening equipment 110 may capture the firstplurality of images in a temporal sequence every 16.67 ms, and theacquisition unit 510 may acquire the second plurality of imagesincluding the first generated image, the fifth generated image, theninth generated image, . . . of the first plurality of images in thetemporal sequence.

In some embodiments, the first image of the second plurality of imagesmay not be the first generated image of the first plurality of image.For example, if the first frame rate is set to be 60 fps, and the secondframe rate is set to be 15 fps, the security screening equipment 110 maycapture the first plurality of images in a temporal sequence every 16.67ms, and the acquisition unit 510 may acquire the second plurality ofimages including the second generated image, the sixth generated image,the tenth generated image, . . . of the first plurality of images in thetemporal sequence.

In some embodiments, the acquisition unit 510 may transmit the firstplurality of images and the second plurality of images to a storage(e.g., the storage device 140, the terminal 130, the storage module 440,or the storage 390). In some embodiments, the acquisition unit 510 mayobtain the first plurality of images from the storage (e.g., the storagedevice 140, the terminal 130, the storage module 440, or the storage390).

In 902, the analysis unit 520 may determine a plurality of processedimages based on the second plurality of images by performing smartalgorithm analysis on each of the second plurality of images. Eachprocessed image of the plurality of processed images may correspond animage of the second plurality of images and an image of the firstplurality of images. As described elsewhere in the present disclosure,the second plurality of images may be in a temporal sequence. The firstimage of the second plurality of images may be the first generated imageof the first plurality of images, and may be processed by the analysisunit 520 to determine a first processed image, similarly to thedescription in operation 602. The second image of the second pluralityof images may be the fifth generated image of the first plurality ofimages. For example, if the first frame rate is set to be 60 fps, andthe second frame rate is set to be 15 fps, the acquisition unit 510 mayacquire the second plurality of images including the first generatedimage, the fifth generated image, the ninth generated image, . . . ofthe first plurality of images in the temporal sequence, and the secondimage of the second plurality of images may be the fifth generated imageof the first plurality of images. Details of the determination theprocessed image may be described elsewhere in the present disclosure.See, e.g., FIG. 6 and the descriptions thereof.

A processed image of the plurality of processed images may include acorresponding image of the second plurality of images and informationrelating to an object identified in the corresponding image, wherein theinformation relating to the object may include one or more pixels of theobject in the corresponding image and a coordinate of the object in thecorresponding image. It should be noted that the one or more pixels ofthe object in the corresponding image may be interchangeably used withone or more pixels of the object in the processed image, and thecoordinate of the object in the corresponding image may beinterchangeably used with a coordinate of the object in the processedimage.

In 903, the distance determination unit 530 may determine a second pixeldistance between the object in two adjacent processed images of theplurality of processed images. In some embodiments, the plurality ofprocessed images may include N processed images, where N is an integerand larger than 1, and may include N−1 sets of two adjacent processedimages. It should be noted that a set of two adjacent processed imagesmay correspond to a set of two adjacent images of the second pluralityof images. And there may be one or more images between the two adjacentimages of the second plurality of images in the first plurality ofimages. For example, the set of two adjacent processed images mayinclude the first processed image corresponding to the first image ofthe second plurality of images, i.e., the first generated image of thefirst plurality of images, and the second processed image correspondingto the second image of the second plurality of images, i.e., the fifthgenerated image of the first plurality of images. Therefore, there arethree images (the second generated image, the third generated image, thefourth generated image) between the two adjacent images of the secondplurality of images in the first plurality of images.

The distance determination unit 530 may determine the second pixeldistance between the object in the set of two adjacent processed images.In some embodiments, the distance determination unit 530 may determineN−1 second pixel distances between the object in the N−1 set of twoadjacent processed images. It should be noted that the second pixeldistance between the object in two adjacent processed images of theplurality of processed images may be interchangeably used as a secondpixel distance between the object in two adjacent images of the secondplurality of images. As used herein, the two adjacent processed imagesmay be any two adjacent processed images corresponding to any twoadjacent images of the second plurality of images. For example, the twoadjacent processed images may include the first processed imagecorresponding to the first image of the second plurality of images,which is the first generated image of the first plurality of images, andthe second processed image corresponding to the second image of thesecond plurality of images, which is the fifth generated image of thefirst plurality of images. For another example, the two adjacentprocessed images may include the second processed image corresponding tothe second image of the second plurality of images, which is the fifthgenerated image of the first plurality of images, and the thirdprocessed image corresponding to the third image of the second pluralityof images, which is the ninth generated image of the first plurality ofimages.

In some embodiments, the two adjacent processed images may include thefirst processed image and the second processed image. The firstprocessed image may include a first coordinate of the object in thefirst image of the second plurality of images, and the second processedimage may include a second coordinate of the object in the second imageof the second plurality of images. The distance determination unit 530may determine the second pixel distance between the object in the twoadjacent processed images (e.g., the first processed image and thesecond processed image) based on the first coordinate of the object inthe first image of the second plurality of images and the secondcoordinate of the object in the second image of the second plurality ofimages.

In some embodiments, the second pixel distance may be a number count ofpixels along one or more directions. In some embodiments, the one ormore directions may include an X-axis direction parallel to the movingdirection of the object or the conveyor belt 113. In some embodiments,the one or more directions may include a Y-axis direction perpendicularto the moving direction of the object or the conveyor belt 113. Forexample, as illustrated in FIG. 13, if the first coordinate of theobject 112-A in the first processed image of the second plurality ofimages (i.e., the first generated image of the first plurality ofimages) is (200, 100), and the second coordinate of the object 112-E inthe second image of the second plurality of images (i.e., the fifthgenerated image of the first plurality of images) is (120, 100), thedistance determination unit 530 may determine that the second pixeldistance D_(p2) between the object in the first processed image and theobject in the second processed image is 80 pixels along the X-axisdirection parallel to the moving direction of the object 112. Foranother example, if the first coordinate of the object 112-A in thefirst processed image of the second plurality of images (i.e., the firstgenerated image of the first plurality of images) is (200, 100), and thesecond coordinate of the object 112-E in the second image of the secondplurality of images (i.e., the fifth generated image of the firstplurality of images) is (120, 96), the distance determination unit 530may determine that the second pixel distance between the object in thefirst processed image and the object in the second processed image is 80pixels along the X-axis direction parallel to the moving direction ofthe object and 4 pixels along the Y-axis direction perpendicular to themoving direction of the object.

In 904, the distance determination unit 530 may determine a first pixeldistance between the object in two adjacent images of the firstplurality of images based on the second pixel distance. In someembodiments, the two adjacent images may be two images captured by thesecurity screening equipment 110 at two adjacent time points. Forexample, the two adjacent images may include the first generated imageand the second generated image captured by the security screeningequipment 110 later than and next to the first generated image. Thedistance determination unit 530 may determine a set of images of thefirst plurality of images corresponding to a set of two adjacentprocessed images of the plurality of processed images. The set of imagesof the first plurality of images may include two images corresponding tothe two adjacent processed images and one or more images between the twoimages in the first plurality of images. The first generated image ofthe set of images and the last generated image of the set of images maybe processed by the analysis unit 520 to determine processed images, andthe other images between the first generated image of the set of imagesand the last generated image of the set of images may not be processedby the analysis unit 520. This may greatly release the computing burdenof the processors and improve the efficiency of the processing, sincethe smart algorithm analysis may need to a lot of computing resource.

For example, the set of two adjacent processed images may include thefirst processed image corresponding to the first image of the secondplurality of images, i.e., the first generated image of the firstplurality of images, and the second processed image corresponding to thesecond image of the second plurality of images, i.e., the fifthgenerated image of the first plurality of images. The set of images ofthe first plurality of images may include the first generated image, thesecond generated image, the third generated image, the fourth generatedimage, and the fifth generated image of the first plurality of images.For another example, the set of two adjacent processed images mayinclude the second processed image corresponding to the second image ofthe second plurality of images, i.e., the fifth generated image of thefirst plurality of images, and the third processed image correspondingto the third image of the second plurality of images, i.e., the ninthgenerated image of the first plurality of images. The set of images ofthe first plurality of images may include the fifth generated image, thesixth generated image, the seventh generated image, the eighth generatedimage, and the ninth generated image of the first plurality of images.

The first pixel distance may be a number count of pixels along one ormore directions. In some embodiments, the one or more directions mayinclude the X-axis direction parallel to the moving direction of theobject or the conveyor belt 113. In some embodiments, the one or moredirections may include the Y-axis direction perpendicular to the movingdirection of the object or the conveyor belt 113. In some embodiments,the first pixel distance between the object in any two adjacent imagesof the first plurality of images may be the same. In some embodiments,the first pixel distance between the object in two adjacent images ofthe first plurality of images may be different to each other.

In some embodiments, the distance determination unit 530 may determinethe first pixel distance between two adjacent images of the set ofimages based on the second pixel distance, the first frame rate and thesecond frame rate set by a user as bellows:

$\begin{matrix}{{D_{p\; 1} = \frac{D_{p\; 2}}{m}},} & (2)\end{matrix}$

where D_(p1) is the first pixel distance between the object in the twoadjacent images of the set of images, D_(p2) is the second pixeldistance between the object in the two processed images of the set ofprocessed images, m is a ratio of the first frame rate to the secondframe rate.

For example, if the first frame rate is 60 fps, and the second framerate is 15 fps, the ratio of the first image to the second image may be4, As illustrated in FIG. 13, the second pixel distance between theobject in the two adjacent processed images of the set of processedimages (e.g., the first processed image and the second processed image,or the second processed image and the third processed image, etc.) mayinclude four first pixel distances between the object in the twoadjacent image of the set of images. The first pixel distance determinedbased on the formula (2) may be an average first pixel distance of fourfirst pixel distances.

In 905, the rendering unit 540 may render an object box in each of thefirst plurality of images. In some embodiments, the object box maysurround the object identified by the analysis unit 520. In someembodiments, the object box may surround part of the object (e.g. acenter of the object) identified by the analysis unit 520. The objectbox may be displayed in accordance with the first plurality of images.In some embodiments, the object box may be configured to remind a user(e.g., security staff) that there is a dangerous thing (the object) in aluggage.

In some embodiments, the rendering unit 540 may render the object box ineach of the first plurality of images based on a coordinate of theobject in the image processed by the analysis unit 520 and/or the firstpixel distance between two adjacent images of the first plurality ofimages. In some embodiments, for each image of the second plurality ofimages, the rendering unit 540 may render the object box based on thecoordinate of the object in the image; for each image of the firstplurality of images other than the second plurality of images, therendering unit 540 may render the object box based on the coordinate ofthe object in an image of the second plurality of images and the firstpixel distance.

For example, for each set of images of the first plurality of images,the first generated image of the set of images and the last generatedimage of the set of images may be processed by the analysis unit 520 todetermine processed images, which include the information of thecoordinate of the object in the image, and the other images between thefirst generated image of the set of images and the last generated imageof the set of images may not be processed by the analysis unit 520. Therendering unit 540 may determine a coordinate of the object in each ofthe other images between the first generated image of the set of imagesand the last generated image of the set of images based on thecoordinate of the object in the first generated image of the set ofimages and the first pixel distance. The rendering unit 540 may thenrender the object box in each image of the set of images based on thecoordinate of the object in the image. Details of the rendering theobject box may be described in the present disclosure. See, e.g., FIG.10 and the descriptions thereof.

In 906, the display module 430 may display the object box in accordancewith the image. In some embodiments, the display module 430 may displaythe image and the object box simultaneously. In some embodiments, thedisplay module 430 may display the first plurality of images and theobject box in each of the first plurality of images according to thefirst frame rate. For example, the display module 430 may display animage once a time and display 60 images per second. In some embodiments,the security screening equipment 110 may be configured to capture avideo including the first plurality of images according to the firstframe rate, and the display module 430 configured to display the videoincluding the first plurality of images and the corresponding object boxin each of the first plurality of images according to the first framerate. In some embodiments, the display module 430 may display the firstplurality of images and the object box in each of the first plurality ofimages according to a third frame rate set by a user. The third framerate may be different with the first frame rate. For example, the thirdframe rate may larger than the first frame rate or less than the firstframe rate.

It should be noted that the above description of process 900 is merelyprovided for the purposes of illustration, and not intended to beunderstood as the only embodiment. For persons having ordinary skills inthe art, various variations and modifications may be conduct under theteaching of some embodiments of the present disclosure. In someembodiments, some operations may be reduced or added. However, thosevariations and modifications may not depart from the protecting of someembodiments of the present disclosure. For example, one or more otheroptional operations (e.g., a storing operation) may be added in theprocess 900. In the storing operation, the image processing device 120may store information and/or data associated with the plurality ofimages, the processed image in a storage device (e.g., the storagedevice 140, the storage module 440, or the storage 390) as describedelsewhere in the present disclosure.

FIG. 10 is a block diagram of an exemplary rendering unit according tosome embodiments of the present disclosure. As illustrated in FIG. 10,the rendering unit 540 may include a coordinate determination sub-unit1010, and a rendering sub-unit 1020.

The coordinate determination sub-unit 1010 may be configured todetermine a second coordinate of an object and/or an object box in animage. In some embodiments, the rendering unit 540 may obtain a firstplurality of images captured by the security screening equipment 110 ina temporal sequence. The analysis unit 520 may process at least oneimage of the first plurality of images to determine a processed image.The at least one image of the first plurality of images may include thefirst generated image of the first plurality of images. The processedimage may include a first coordinate of the object in the at least oneimage. In some embodiments, the coordinate determination sub-unit 1010may be configured to determine the second coordinate of the object inthe image which is not processed by the analysis unit 520 based on thefirst coordinate of the object in a previously generated image which isprocessed by the analysis unit 520, a first pixel distance between theobject in two adjacent images of the first plurality of images, and anumber count of images generated between the image and the previouslygenerated image which is processed by the analysis unit 520. As usedherein, the first coordinate of the object in an image may refer to acoordinate of the object in the image which is processed and determinedby the analysis unit 520, and the second coordinate of the object in animage may refer to a coordinate of the object in the image which isdetermined by the coordinate determination sub-unit 1010, wherein theimage is not processed by the analysis unit 520.

In some embodiments, as illustrated in FIG. 13, the image including theobject 112-B may not be processed by the analysis unit 520, and thepreviously generated image including the object 112-A may be processedby the analysis unit 520. For example, the first coordinate of theobject 112-A in the previously generated image may be determined by theanalysis unit 520 as (200,100), the first pixel distance may be 20 alongthe negative X-axis direction, and the number count of images generatedbetween the image and the previously generated image is 0. Thecoordinate determination sub-unit 1010 may determine that the secondcoordinate of the object 112-B in the image as (180, 100). For anotherexample, the first coordinate of the object 112-A in the previouslygenerated image may be determined by the analysis unit 520 as (200,100),the first pixel distance may be 20 along the negative X-axis directionand 1 along the negative Y-axis direction, and the number count ofimages generated between the image and the previously generated image is0. The coordinate determination sub-unit 1010 may determine that thesecond coordinate of the object 112-B as (180, 99).

The rendering sub-unit 1020 may be configured to render an object box inan image based on a coordinate of the object in the image. The objectbox may surround the object. The object may be a knife, a gun, acigarette lighter, or the like, and may have various shapes. In someembodiments, the object box may have a regular shape as the same withthe shape of the identified object. In some embodiments, the object boxmay have a shape of rectangle, square, triangle, circle, oval, orirregular shape. The rendering sub-unit 1020 may render the object boxusing a color different from the image for alert, e.g., red, green,yellow, etc. The rendering sub-unit 1020 may further render the objectbox in a twinkling manner.

In some embodiments, the rendering sub-unit 1020 may render the objectbox based on the coordinate of the object in the image. In someembodiments, the object box 1201 may include side of the rangerepresenting the coordinate of the object 112. In some embodiments, theobject box 1201 may surround a region larger than the range representingthe coordinate of the object 112. For example, the range representingthe coordinate of the object 112 may be a rectangle region defined by(X_(min), X_(max), Y_(min), Y_(max)), and the object box 1201 may be arectangle defined by (X_(min)−N_(x1), X_(max)+N_(x2), Y_(min)−N_(Y1)Y_(max)+N_(Y2)), wherein N_(x1), N_(x2), N_(Y1), N_(Y2) may be anyinteger respectively. For example, the range representing the coordinateof the object 112 may be a square region defined by center point (X_(A),Y_(A)) and side-length L=2*D_(max), or a circle region defined by centerpoint (X_(A), Y_(A)) and the radius D_(max), and the object box 1201 maybe a circle defined by the center point (X_(A), Y_(A)) and a radiusD_(max)+D, wherein D may be any integer.

Sub-units of the rendering unit 540 may be connected to or communicatewith each other via a wired connection or a wireless connection. Thewired connection may include a metal cable, an optical cable, a hybridcable, or the like, or any combination thereof. Two or more of thesub-units may be combined into a single sub-unit, and any one of thesub-units may be divided into two or more blocks. For example, thecoordinate determination sub-unit 1010 and the rendering sub-unit 1020may be combined into a single unit that may be configured to determinethe coordinate of the object in the image and render the object box inthe image,

FIG. 11A illustrates a schematic image captured by the securityscreening system according to some embodiments of the presentdisclosure. In some embodiments, the security screening equipment 110may capture a first plurality of images in a temporal sequence when theluggage located on the conveyor belt 113 is moving. The image may be animage of the first plurality of images including an object 112. Itshould be noted that an imaging range of the security screeningequipment 110 may be fixed while the luggage is moving on the conveyorbelt 113, resulting that the position of the luggage in the firstplurality of image may be various. For example, the coordinate of theobject 112 in the first plurality of image may be various.

FIG. 11B illustrates a schematic processed image determined by thesecurity screening system based on the image shown in FIG. 11A accordingto some embodiments of the present disclosure. In some embodiments, theanalysis unit 520 may process the image as illustrated in FIG. 11A, anddetermine a processed image as illustrated in FIG. 11B. The processedimage may include information relating to the object 112. Theinformation relating to the object 112 may include one or more pixels ofthe object 112 and the coordinate of the object 112 in the image.

FIG. 12 illustrates a schematic image shown in FIG. 11A and the objectbox displayed on a screen according to some embodiments of the presentdisclosure. It should be noted that an imaging range of the securityscreening equipment 110 may be fixed while the luggage is moving on theconveyor belt 113, resulting that the position of the luggage and thecoordinate of the object 112 in the image of the first plurality ofimage may be various. The object box 1201 may also be rendered ondifferent position in the image according to the coordinate of theobject 112 and displayed in accordance with the image on the screensimultaneously. The object box 1201 may rendered using a color differentfrom the image for alert, e.g., red, green, yellow, etc. The object box1201 may be rendered and displayed in a twinkling manner.

FIG. 13 is a schematic diagram illustrating determination of the firstpixel distance between an object in two adjacent images according tosome embodiments of the present disclosure. As illustrated in FIG. 13,five temporal images of the first plurality of images are overlappingwith each other. The object 112-A is in the first generated image of thefive temporal images; the object 112-B is in the second generated imageof the five temporal images; the object 112-C is in the third generatedimage of the five temporal images; the object 112-D is in the fourthgenerated image of the five temporal images; the object 112-E is in thefifth generated image of the five temporal images. It should be notedthat the objects 112-A, 112-B, 112-C, 112-D, 112-E is the same object112, and the coordinate of the object 112 in each of the five temporalimages may be different with each other due to the movement of theobject 112 located on the conveyor belt 113 along the direction ofnegative X-axis direction. In order to release the computing burden ofthe processors and improve the efficiency of the processing, theprocessing module 420 may acquire the first generated image of the fivetemporal images and the fifth generated image of the five temporalimages to process. The processing module 420 may determine a firstcoordinate of the object 112-A in the first generated image, anddetermine a first coordinate of the object 112-E in the fifth generatedimage. The processing module 420 may determine a second pixel distanceD_(p2) between the object 112 in two adjacent processed images based onthe first coordinate of the object 112-A in the first generated imageand the first coordinate of the object 112-E in the fifth generatedimage. The processing module 420 may further determine a first pixeldistance based on the second pixel distance and a number count of imagesbetween the two adjacent processed images (e.g., the first generatedimage and the fifth generated image). The processing module 420 maydetermine a second coordinate of the object 112 in the images betweenthe two adjacent processed images.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “module,” “unit,” “component,” “device,” or “system.”Furthermore, aspects of the present disclosure may take the form of acomputer program product embodied in one or more computer readable mediahaving computer readable program code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or thelike, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that may communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device. Program code embodied on acomputer readable signal medium may be transmitted using any appropriatemedium, including wireless, wireline, optical fiber cable, RF, or thelike, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE. Emerald, C++, C #, VB.NET, Python or the like, conventional procedural programming languages,such as the “C” programming language, Visual Basic, Fortran 2003, Perl,COBOL 2002, PHP, ABAP, dynamic programming languages such as Python,Ruby and Groovy, or other programming languages. The program code mayexecute entirely on the user's computer, partly on the user's computer,as a stand-alone software package, partly on the user's computer andpartly on a remote computer or entirely on the remote computer orserver. In the latter scenario, the remote computer may be connected tothe user's computer through any type of network, including a local areanetwork (LAN) or a wide area network (WAN), or the connection may bemade to an external computer (for example, through the Internet using anInternet Service Provider) or in a cloud computing environment oroffered as a service such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution, e.g., an installationon an existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various embodiments. This method ofdisclosure, however, is not to be interpreted as reflecting an intentionthat the claimed object matter requires more features than are expresslyrecited in each claim. Rather, claim object matter lie in less than allfeatures of a single foregoing disclosed embodiment.

I claim:
 1. A system, comprising: at least one storage medium includinga set of instructions; and at least one processor in communication withthe at least one storage medium, wherein when executing the set ofinstructions, the at least one processor effectuates operationscomprising: obtaining a plurality of images, in a temporal sequence,each of the plurality of images relating to an object; obtaining a firstprocessed image by performing a smart algorithm analysis on a firstimage in the plurality of images, the smart algorithm analysis includingidentifying the object in the first image and determining a firstcoordinate of the object in the first image; determining a first pixeldistance between the object in two adjacent images in the plurality ofimages; and rendering an object box for the object in each of theplurality of images for display based on the first coordinate of theobject in the first image and the first pixel distance; wherein indetermining the first pixel distance between the object in two adjacentimages, the at least one processor effectuates further operationscomprising: acquiring a second image from the plurality of images,wherein there is one or more images between the first image and thesecond image; determining a second processed image by perform the smartalgorithm analysis on the second image; determining a second pixeldistance between the object in the first image and the object in thesecond image based on the first coordinate of the object in the firstimage and a second coordinate of the object in the second image;determining a number count of the one or more images between the firstimage and the second image; and determining the first pixel distancebased on the second pixel distance and the number count of the one ormore images between the first image and the second image.
 2. The systemof claim 1, further comprising an image capture device, wherein theplurality of images are obtained from a video captured by the imagecapture device.
 3. The system of claim 1, wherein the number count ofthe one or more images between the first image and the second image isdetermined based on a first frame rate of the video and a second framerate of processed image, the first frame rate and the second frame ratebeing preset by a user.
 4. The system of claim 1, wherein in renderingan object box in each of the plurality of images, the at least oneprocessor effectuates further operations comprising: determining a thirdcoordinate of the object in each of the one or more images between thefirst image and the second image based on the first coordinate of theobject in the first image and the first pixel distance; rendering anobject box in the first image based on the first coordinate of theobject in the first image; rendering an object box in each of the one ormore images between the first image and the second image based on thethird coordinate; and rendering an object box in the second image basedon the second coordinate of the object in the second image.
 5. Thesystem of claim 1, wherein in rendering an object box in each of theplurality of images, the at least one processor effectuates furtheroperations comprising: determining a fourth coordinate of the object ineach of the plurality of images other than the first image based on thefirst coordinate of the object in the first image and the first pixeldistance; rendering an object box in the first image based on the firstcoordinate of the object in the first image; and rendering an object boxin each of the plurality of images other than the first image based onthe fourth coordinate of the object.
 6. The system of claim 1, wherein ashape of the object box includes one of rectangle, square, triangle,circle, oval, or irregular shape.
 7. The system of claim 1, wherein ashape of the object box includes a contour of the object.
 8. The systemof claim 1, wherein the smart algorithm analysis comprising aconvolutional neutral network (CNN), Region-based Convolutional Network(R-CNN), Spatial Pyramid Pooling Network (SPP-Net), Fast Region-basedConvolutional Network (Fast R-CNN), Faster Region-based ConvolutionalNetwork (Faster R-CNN).
 9. The system of claim 3, further comprising ascreen configured to display the plurality of images and the object boxin each of the plurality of images in the temporal sequence.
 10. Thesystem of claim 9, wherein the screen displays the plurality of imagesand the object box according to a third frame rate.
 11. A methodimplemented on at least one machine each of which has at least oneprocessor and a storage device, comprising: obtaining, by the at leastone processor, a plurality of images in a temporal sequence, each of theplurality of images relating to an object; obtaining, by the at leastone processor, a first processed image by performing a smart algorithmanalysis on a first image in the plurality of images, the smartalgorithm analysis including identifying the object in the first imageand determining a first coordinate of the object in the first image;determining, by the at least one processor, a first pixel distancebetween the object in two adjacent images in the plurality of images;and rendering, by the at least one processor, an object box for theobject in each of the plurality of images for display based on the firstcoordinate of the object in the first image and the first pixeldistance; wherein the determining the first pixel distance between theobject in two adjacent images further comprising: acquiring, by the atleast one processor, a second image from the plurality of images,wherein there is one or more images between the first image and thesecond image; determining, by the at least one processor, a secondprocessed image by perform the smart algorithm analysis on the secondimage; determining, by the at least one processor, a second pixeldistance between the object in the first image and the object in thesecond image based on the first coordinate of the object in the firstimage and a second coordinate of the object in the second image;determining, by the at least one processor, a number count of the one ormore images between the first image and the second image; anddetermining, by the at least one processor, the first pixel distancebased on the second pixel distance and the number count of the one ormore images between the first image and the second image.
 12. The methodof claim 11, the at least one machine further comprising an imagecapture device, wherein the plurality of images are obtained from avideo captured by the image capture device.
 13. The method of claim 11,wherein the number count of the one or more images between the firstimage and the second image is determined based on a first frame rate ofthe video and a second frame rate of processed image, the first framerate and the second frame rate being preset by a user.
 14. The method ofclaim 11, wherein the rendering an object box in each of the pluralityof images further comprising: determining, by the at least oneprocessor, a third coordinate of the object in each of the one or moreimages between the first image and the second image based on the firstcoordinate of the object in the first image and the first pixeldistance; rendering, by the at least one processor, an object box in thefirst image based on the first coordinate of the object in the firstimage; rendering, by the at least one processor, an object box in eachof the one or more images between the first image and the second imagebased on the third coordinate; and rendering, by the at least oneprocessor, an object box in the second image based on the secondcoordinate of the object in the second image.
 15. The method of claim11, wherein the rendering an object box in each of the plurality ofimages further comprising: determining, by the at least one processor, afourth coordinate of the object in each of the plurality of images otherthan the first image based on the first coordinate of the object in thefirst image and the first pixel distance; rendering, by the at least oneprocessor, an object box in the first image based on the firstcoordinate of the object in the first image; and rendering, by the atleast one processor, an object box in each of the plurality of imagesother than the first image based on the fourth coordinate of the object.16. A non-transitory computer-readable medium storing instructions, theinstructions, when executed by a computing device including at least oneprocessor, causing the computing device to implement a method, themethod comprising: obtaining, by the at least one processor, a pluralityof images in a temporal sequence, each of the plurality of imagesrelating to an object; obtaining, by the at least one processor, a firstprocessed image by performing a smart algorithm analysis on a firstimage in the plurality of images, the smart algorithm analysis includingidentifying the object in the first image and determining a firstcoordinate of the object in the first image; determining, by the atleast one processor, a first pixel distance between the object in twoadjacent images in the plurality of images; and rendering, by the atleast one processor, an object box for the object in each of theplurality of images for display based on the first coordinate of theobject in the first image and the first pixel distance; wherein thedetermining the first pixel distance between the object in two adjacentimages further comprising: acquiring, by the at least one processor, asecond image from the plurality of images, wherein there is one or moreimages between the first image and the second image; determining, by theat least one processor, a second processed image by perform the smartalgorithm analysis on the second image; determining, by the at least oneprocessor, a second pixel distance between the object in the first imageand the object in the second image based on the first coordinate of theobject in the first image and a second coordinate of the object in thesecond image; determining, by the at least one processor, a number countof the one or more images between the first image and the second image;and determining, by the at least one processor, the first pixel distancebased on the second pixel distance and the number count of the one ormore images between the first image and the second image.